How artificial intelligence is helping detect tuberculosis in remote areas

Researchers are training artificial intelligence to identify tuberculosis on chest X-rays, an initiative that could help screening and evaluation efforts in TB-prevalent areas lacking access to radiologists.

The findings are part of a study published online in the journal Radiology.

“An artificial intelligence solution that could interpret radiographs for the presence of TB in a cost-effective way could expand the reach of early identification and treatment in developing nations,” study co-author Paras Lakhani, MD, from Thomas Jefferson University Hospital in Philadelphia, wrote in the journal.

For the study, Lakhani and his colleague, Baskaran Sundaram, MD, obtained 1,007 X-rays of patients with and without active TB. The cases consisted of multiple chest X-ray datasets from the National Institutes of Health, the Belarus Tuberculosis Portal, and Thomas Jefferson University Hospital.

The cases were used to train two models – AlexNet and GoogLeNet – which learned from TB-positive and TB-negative X-rays. The models’ accuracy was tested on 150 cases.